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Correlation Errors: When Teams (and Fans) Misread Data
Race Analysis

Correlation Errors: When Teams (and Fans) Misread Data

Most F1 arguments about “what the data says” aren’t fights about numbers—they’re fights about assumptions. The tricky part is that modern F1 data is highly connected: pace affects track position, track position affects tyre life, tyre life affects strategy, strategy affects points, and points affect how a season feels in hindsight. When you miss those connections (or count them twice), even a well-built model can produce confident-looking outputs for the wrong reasons.

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What Long-Run Data Tells Simulators That Short Runs Can’t
Race Analysis

What Long-Run Data Tells Simulators That Short Runs Can’t

Single-lap pace is seductive because it’s clean: a lap time, a delta, a headline. But if you’re building an F1 points forecast—or even just stress-testing who can realistically stay in a title fight—short runs are the wrong place to anchor your confidence. Over a season, the standings are shaped less by the best lap a car can produce and more by what it repeats: stint after stint, track after track, under imperfect conditions.

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Pre-Season Lap Times vs Season Simulations
Race Analysis

Pre-Season Lap Times vs Season Simulations

Pre-season testing is the most over-interpreted dataset in Formula 1. It’s the first time we see new cars run meaningful mileage, and it’s natural to anchor on what looks measurable: the lap time. But a championship isn’t awarded for the fastest Thursday in February—it’s awarded for accumulating points across a season full of reliability swings, upgrades, track-specific strengths, and inevitable randomness.

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